Please use this identifier to cite or link to this item: http://cmuir.cmu.ac.th/jspui/handle/6653943832/61039
Title: Optimal economic dispatch for power generation using artificial neural network
Authors: S. Panta
S. Premrudeepreechacharn
S. Nuchprayoon
C. Dechthummarong
S. Janjommanit
S. Yachiangkam
Keywords: Energy
Engineering
Issue Date: 1-Dec-2007
Abstract: This paper presents an optimal economic dispatch of electrical power plants by using back-propagation neural networks. The method of economic dispatch for generating units at different loads must have total fuel cost at the minimum point. Thcrc arc many conventional methods that can use to solve economic dispatch problem such as Lagrange multiplier method, Lamda iteration method and Newton-Raphson method. However, an obstacle in optimal economic dispatch of conventional methods is the changed load. They arc necessary to find thc optimal economic dispatch from time to time. Moreover, they need a lot of time to repeat calculation for a new solution again. This paper presents backpropagation neural networks model to carry out instead the conventional Lamda iteration method. It is compared with the experimental results of electrical power system of 3 10 and 20 generating units respectively. The testing results of the back-propagation neural networks are compared with the Lamda iteration method by testing the teaching data and non-teaching data. It shows clearly that the back-propagation neural networks can find out the solutions accurately and use time to calculate less than other systems that are tested. Error of prediction will be increased slightly by the number of generating units in electrical power plants because it needs to learn a lot of input and output data in the neural network dramatically. © 2007 RPS.
URI: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=51349114625&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/61039
Appears in Collections:CMUL: Journal Articles

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